SpecGMM: Integrating Spectral analysis and Gaussian Mixture Models for taxonomic classification and identification of discriminative DNA regions.

ABSTRACT:

Motivation

Genomic signal processing (GSP), which transforms biomolecular sequences into discrete signals for spectral analysis, has provided valuable insights into DNA sequence, structure, and evolution. However, challenges persist with spectral representations of variable-length sequences for tasks like species classification and in interpreting these spectra to identify discriminative DNA regions.

Results

We introduce SpecGMM, a novel framework that integrates sliding window-based Spectral analysis with a Gaussian Mixture Model to transform variable-length DNA sequences into fixed-dimensional spectral representations for taxonomic classification. SpecGMM's hyperparameters were selected using a dataset of plant sequences, and applied unchanged across diverse datasets, incl

SUBMITTER: Jaiswal S 

PROVIDER: S-EPMC11631429 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature